If your YouTube stream keeps an encoder busy around the clock, C7i is the better-supported starting point; AWS classifies video encoding as a suitable C7i workload and recommends fixed-performance instances for consistently high CPU use. T3 can suit variable workloads, but you need to account for CPU credits and network bursting rather than assume its peak capacity is sustained capacity.
There is no cited apples-to-apples benchmark establishing that either family wins for a particular YouTube encoder configuration. Choose by testing the exact instance size, encoder settings and destination path you intend to use, then compare its sustained behaviour and full cost.
Describe the workload before choosing an instance
“Streaming to YouTube” can mean very different workloads. A static slideshow with modest motion may be light to encode, while a high-resolution nature loop with foliage, water or camera movement can keep an encoder busier. Multiple output renditions, overlays, audio processing or packaging add more work. The instance family alone does not tell you how much compute your particular stream needs.
Start by writing down what the host must do continuously. Note the source file or live input, codec, resolution, frame rate, bitrate, number of outputs, overlays and any playlist or packaging step. Include the intended YouTube ingest destination and region. If you are streaming a recorded programme, check that the loop has no gaps and that its encoder can repeat it without building up a backlog. The practical questions are whether the host can encode the source at its chosen settings, keep sending data, and recover cleanly if the process fails.
Separate encoding from other components. A YouTube stream involves the encoder, outbound network path and YouTube ingest; a larger architecture may also include packaging or distribution stages. AWS's live-streaming planning guide treats encoding and packaging separately from distribution. That is a useful reminder that an EC2 instance choice addresses only the work assigned to that host.
Write down acceptable operating behaviour too. For example, a channel may be willing to use a smaller output resolution if CPU use remains high, but not to let frames queue until the stream falls behind. Another channel may have a fixed picture and bitrate requirement and instead need a larger instance. Those are different sizing problems. A clear workload description prevents you from treating an instance label or a published maximum bandwidth as a promise of stream quality.
Fixed performance or burstable capacity
C7i is compute-optimised, and AWS names video encoding among the workloads it suits. For continuous CPU-heavy work, that makes C7i a more natural starting point than a burstable general-purpose instance. AWS's instance-type guidance also recommends fixed-performance instances for consistently high CPU workloads. This classification supports the choice of a family to test; it does not prove that any particular C7i size can handle your encoder settings.
T3 is a general-purpose burstable family. Its design lets an instance use more CPU for periods than its baseline, with the difference accounted for through credits. That can be useful when the encoder is idle or lightly loaded much of the time and has occasional bursts of work. A stream that continuously encodes video is different: its CPU demand may persist for hours, so the baseline and the credit balance matter, not just the ability to burst at the start.
The families overlap in some nominal sizes, but equal vCPU counts do not make them equivalent choices. For example, AWS lists both t3.medium and c7i.large with 2 vCPUs and 4 GiB of memory. Their CPU performance models differ, and their network specifications differ as well. Those figures describe instance characteristics, not the number of frames your own encoder will process or whether YouTube will receive a stable stream.
Use C7i as a first candidate when your test shows that encoding is persistently CPU-heavy. Consider T3 where demand is genuinely intermittent and you have checked the credit model, network behaviour and any charges for sustained use. If the stream is meant to run continuously, do not choose T3 merely because a short trial starts successfully. A brief test can finish while credits are available and say little about behaviour later.
T3 CPU credits and network bursting
On T3, credits accumulate while CPU use is below the baseline and are spent when use goes above it. AWS defines one CPU credit as one vCPU running at 100% utilisation for one minute. The baseline, vCPU count, memory and network characteristics vary by T3 size, so a figure for one size should not be applied to the whole family. AWS says T3 instances start in Unlimited mode by default; sustained high use in that mode can incur additional charges under AWS's terms.
The distinction between a burst and a continuous load is central. Imagine your encoder uses little CPU while waiting between scheduled segments, then works hard to process a new segment. Credits may help absorb those busy periods. If it instead spends most of the day encoding a moving image at a steady rate, the workload can remain above baseline. Check credit balance and CPU utilisation over a long enough run to include the ordinary high-demand period, rather than looking only at startup or a short preview.
Network bursting is a separate issue from CPU credits. AWS's network-bandwidth documentation explains that network performance depends on instance size and traffic path. Some smaller instances marked “up to” can burst above a baseline through network I/O credits; traffic routed through an internet gateway is subject to the documented constraints. A headline maximum therefore does not establish that an instance will sustain the outbound throughput your stream requires in your region.
Do not infer network suitability from a CPU-family comparison alone. Check the exact size's published network characteristics, then test the outbound path used by the stream. The bitrate is the stream's encoded data rate, not a complete account of every protocol and transport overhead. AWS's EC2 network bandwidth guidance is the primary reference for instance network limits and burst behaviour; validate actual delivery rather than converting “up to” into a guarantee.
Match the size to the encoder
Select a small set of candidate sizes based on required memory and the CPU workload, not on a family name alone. AWS's T3 table, for example, lists t3.medium with 2 vCPUs, 4 GiB of memory, a 20% CPU baseline per vCPU and up to 5 Gbps of network burst bandwidth. It lists t3.2xlarge with 8 vCPUs, 32 GiB, a 40% baseline per vCPU and up to 2.78 Gbps. These examples illustrate why assumptions should not be carried from one T3 size to another.
The C7i table lists c7i.large at 2 vCPUs and 4 GiB, with up to 12.5 Gbps network bandwidth; c7i.8xlarge at 32 vCPUs and 64 GiB with 12.5 Gbps; and c7i.48xlarge at 192 vCPUs and 384 GiB with up to 50 Gbps. Larger sizes offer substantially more total compute and memory, but the listed network limits differ and do not demonstrate application-level throughput. These examples are not a recommendation to start with a large size. They show why you should inspect the specification for the exact candidate you are considering.
| Question | T3 | C7i |
|---|---|---|
| Performance model | Burstable CPU, with baseline and credits to understand | Compute-optimised, fixed-performance starting point for sustained CPU work |
| Workload fit | Variable demand, if bursts and sustained-use terms fit | Consistently CPU-heavy work such as video encoding |
| Network assessment | Check the exact size, burst bandwidth and traffic path | Check the exact size and traffic path; published maximums are not end-to-end tests |
| Main test concern | Can the workload stay within an acceptable credit and cost pattern? | Does the chosen size have enough CPU and memory for the actual encoder? |
This is a way to narrow candidates, not an encoder performance ranking. Read the current T3 specifications and C7i specifications for the sizes and region you intend to test. Instance tables change, and the pages' specifications cannot tell you how your particular input, codec, encoder version or settings will behave.
If you are still deciding on output settings, establish those separately before sizing. The guide to bitrate settings for 24/7 bhajans can help you think through the relationship between stream settings and the intended channel. If your content is a demanding high-resolution loop, the checks in how to loop a 4K nature video without lag are relevant to the source and playback side. Neither substitutes for measuring the encoder on the selected host.
Test with real stream settings and destination
Build a matched test: use the same source, encoder version, codec, resolution, frame rate, bitrate, overlays and audio settings on each candidate. Keep the YouTube ingest destination and network route consistent. Change one variable at a time, starting with instance size or family; otherwise, if one test behaves differently, you will not know whether the cause was the CPU, encoder preset or route.
Run the test long enough to represent sustained operation, not only a short connection check. Observe CPU utilisation, memory use, encoder-reported missed or delayed frames, output bitrate, reconnects and whether the process remains current with the source. On T3, include credit balance and any surplus-credit charges in the observation. On either family, watch for network drops and compare outbound throughput over time. Keep a record of the exact instance, region, settings and run period so a later result is meaningful.
Test with the actual destination path. A test against a nearby endpoint or a local file does not establish performance to YouTube ingest from the region you plan to use. Likewise, a high published network limit is not evidence that the full route will carry a continuous stream. YouTube's live encoder settings guidance is a useful reference for supported stream configuration; check the current official advice and your channel's stream status while testing.
If the stream starts but remains offline, troubleshoot the encoder and key path as well as the instance. A resource problem and a configuration problem can look similar from the viewer's side. The checks in YouTube RTMP stream starts but stays offline help separate those causes. If YouTube reports an error, use the OBS RTMP error 400 troubleshooting guide as a configuration-focused reference rather than immediately resizing the EC2 host.
A useful test does not have to prove that a stream can never fail. It should reveal whether the candidate has headroom under the intended settings, whether CPU demand is persistent or intermittent, and whether the outbound path behaves acceptably. Repeat a run if conditions materially differ, and avoid treating one successful start as a reliability guarantee.
Stability, headroom and the full cost
Headroom is capacity left over when the stream is doing its normal work. If CPU use stays close to the limit, an overlay, a busier scene or a restart may push encoding into delayed frames. If a T3 instance depends on credit balance to sustain that work, headroom can narrow as the run continues. A C7i candidate may provide a clearer basis for a steady compute-heavy test, but you still need to select a suitable size and inspect its observed behaviour.
Compare stability using the same signs on each instance: sustained CPU levels, memory pressure, encoder warnings, output consistency, reconnects and process recovery. Keep the stream settings constant. A larger instance can reduce resource pressure, but it does not correct an incorrect key, unsupported encoder configuration or weak network route. The YouTube RTMP troubleshooting guide is useful when symptoms point towards the stream configuration rather than capacity.
Cost needs the same careful boundary. Compare the hourly rate for the exact size, region, operating system and purchase arrangement you expect to use, and account for the actual run time. For T3, include any surplus-credit charges that apply to sustained use in Unlimited mode. Do not take a displayed Linux/Unix price for US East (Northern Virginia) as a universal quote; it is specific to that page context. Re-check AWS's current pricing and terms before committing, as neither a family label nor an old estimate is enough to calculate your bill.
EC2 is not necessarily the whole bill for a live-streaming system. Depending on the design, encoding, packaging, storage, data transfer and distribution can be separate cost items. AWS's deployment planning material treats these parts separately. Work out which components your own setup uses and avoid attributing an end-to-end cost to the instance's hourly price alone. This is especially important if you compare a self-managed host with an approach where another party handles some of the streaming work.
Choose from measured behaviour
For a stream that keeps the CPU busy, begin your evaluation with C7i sizes that meet the encoder's memory and compute needs. That choice follows AWS's workload classification, not a claim that C7i has won a YouTube benchmark. If measured CPU demand is low for long stretches and rises only in bursts, a T3 candidate may be appropriate after you have checked its baseline, credit trend, sustained-use charges and network behaviour.
Make the final choice from a short test record. For each candidate, capture instance type and size, region, stream settings, duration, CPU and memory observations, T3 credit behaviour where relevant, outbound network behaviour, encoder warnings and the applicable cost estimate. Reject a candidate that only works while the conditions are unusually favourable. If neither family provides suitable headroom at a sensible cost, revise the encoder settings or consider a different approach rather than forcing a family choice.
An EC2 host suits you if you want to manage the instance, operating system, encoder, stream key, monitoring and recovery process yourself. That control comes with operational work: you are responsible for configuration, updates, alerting and checking what happens after a failure. If you would rather not keep your own computer switched on or manage an EC2 encoder process, StreamNeo removes that specific hosting and restart burden by taking an uploaded video and running it as a YouTube live stream; it does not supply or manage EC2 instances.
Before committing, compare the operating options on the pricing page. When the file and channel are ready, start free — 24-hour trial, no card.
FAQ
Is C7i always faster than T3 for YouTube streaming?
There is no cited matched benchmark for a specific YouTube encoder setup, so the family names alone cannot establish that result. AWS positions C7i for compute-heavy work such as video encoding, while T3 is burstable. Test the exact size and settings you plan to use.
Can a T3 instance run a continuous stream?
It may run one, but you should check whether its CPU demand stays above baseline, how credits change, and whether sustained use in Unlimited mode adds charges under AWS's terms. Check the exact size's network behaviour too. A successful short run is not enough to judge a continuous workload.
Does a published network bandwidth figure guarantee the stream will reach YouTube?
No. AWS's network figures depend on instance size and traffic path, and some “up to” values involve bursting. Validate sustained outbound behaviour from the selected region to your intended YouTube ingest destination.
Should I choose an instance based only on the hourly price?
No. Compare the exact size and region, any applicable T3 surplus-credit charges, and the other parts of your streaming architecture. AWS's planning guidance separates encoding and packaging from distribution, so EC2 hourly cost alone may not describe the full service cost.